def acc_plot_fn(ax1, ax2, x, freq):
s = np.square(x[:, 2:])
mag = np.sum(s, axis=1)
mag = np.column_stack((x[:, 1], mag))
plot_time_domain(ax1, mag)
plot_freq_domain(ax2, mag[:, 1], freq)
def output(x):
if args["export_csv"]:
np.savetxt(args["acc_csv"], x, delimiter=',')
if int(args["type"][0]) == 0:
s = np.square(x[:, 1:])
mag = np.sum(s, axis=1)
mag = np.column_stack((x[:, 0], mag))
plot_time_domain(ax1, mag)
plot_freq_domain(ax2, mag[:, 1], ACC_FS)
def output(x):
if args["export_csv"]:
np.savetxt(args["ppg_csv"], x, delimiter=',')
if int(args["type"][0]) == 12:
plot_time_domain(ax1, x)
plot_freq_domain(ax2, x[:, 1], PPG_FS_512)
def default_plot_fn(ax1, ax2, x, freq):
plot_time_domain(ax1, x[:, 1:])
plot_freq_domain(ax2, x[:, 2], freq)
else:
size = len(ppg_data)
ppg_data = ppg_data[start:size]
# Read annotation file
annot = []
if args.annotation_file:
annot_f = open(args.annotation_file)
annot = parse_annotation(annot_f)
filtered_ecg_data = ecg_data[:,1]
filtered_ecg_data = high_pass_filter(filtered_ecg_data, ECG_FS, HIGH_PASS_CUTOFF)
filtered_ecg_data = low_pass_filter(filtered_ecg_data, ECG_FS, LOW_PASS_CUTOFF)
filtered_ecg_data = np.column_stack((ecg_data[:,0], filtered_ecg_data))
filtered_ppg_data = ppg_data[:,1]
filtered_ppg_data = high_pass_filter(filtered_ppg_data, PPG_FS_512, HIGH_PASS_CUTOFF)
filtered_ppg_data = low_pass_filter(filtered_ppg_data, PPG_FS_512, LOW_PASS_CUTOFF)
filtered_ppg_data = np.column_stack((ppg_data[:,0], filtered_ppg_data))
fig = plot.figure()
ax1 = fig.add_subplot(2, 1, 1)
ax2 = fig.add_subplot(2, 1, 2, sharex=ax1)
plot_time_domain(ax1, filtered_ppg_data, color='blue')
plot_time_domain(ax2, filtered_ecg_data, color='black')
plot_annotation(ax1, annot)
plot_annotation(ax2, annot)
plot.show()